# Statistical Inference of Optimal Allocations I: Regularities and their Implications

arXiv: https://arxiv.org/abs/2403.18248 · HTML: https://mlsanity.com/paper/2403.18248/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 75 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Conservative set valued fields, automatic differentiation, stochastic gradient method and deep learning](https://mlsanity.com/paper/1909.10300/) (2019)
- [Offline Multi-Action Policy Learning: Generalization and Optimization](https://mlsanity.com/paper/1810.04778/) (2018)
- [Residual Weighted Learning for Estimating Individualized Treatment Rules](https://mlsanity.com/paper/1508.03179/) (2015)
- [Policy Learning with Asymmetric Counterfactual Utilities](https://mlsanity.com/paper/2206.10479/) (2022)
- [The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages](https://mlsanity.com/paper/1512.05635/) (2015)
- [Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines](https://mlsanity.com/paper/1807.06711/) (2018)

## Abstract (arXiv)

In this paper, we develop a functional differentiability approach for solving statistical optimal allocation problems. We derive Hadamard differentiability of the value functions through analyzing the properties of the sorting operator using tools from geometric measure theory. Building on our Hadamard differentiability results, we apply the functional delta method to obtain the asymptotic properties of the value function process for the binary constrained optimal allocation problem and the plug-in ROC curve estimator. Moreover, the convexity of the optimal allocation value functions facilitates demonstrating the degeneracy of first order derivatives with respect to the policy. We then present a double / debiased estimator for the value functions. Importantly, the conditions that validate Hadamard differentiability justify the margin assumption from the statistical classification literature for the fast convergence rate of plug-in methods.